When Corporate Antibodies Attack Innovation, This Is How Banks Fight Back

The connection between abundance and technology may have been immortalized in a TED Talk by Peter Diamandis, but that hasn’t stopped the fear-based conversations about robots greeting us in hotels or drone fleets delivering our packages.

Michael Leadbetter, head of strategy at ExO Works, picked up where that TED talk left off during a presentation at the recent SAP Financial Services Innovation Summit held at the SAP Leonardo Center in New York. Here’s a summary of Leadbetter’s thoughts on why AI, machine learning, and other exponential technology advancements aren’t all bad for society, and how established financial institutions can learn to innovate.

Cashing in on singularity and abundance

Leadbetter works with Singularity University, an organization in Silicon Valley that educates leaders on how exponential technologies can address the world’s problems. Co-founded by Peter Diamandis, Singularity’s self-described mission is to positively impact the lives of one billion people. Diamandis wrote a book that said exponential technology will generate sufficient abundance to raise global living standards. Jumping off from this concept of singularity and abundance, Leadbetter outlined what he saw as the ten attributes of the exponential organization.

“Large corporations don’t like doing disruption, and they find innovation difficult. Corporate antibodies attack you the minute you try to innovate,” said Leadbetter. “Exponential organizations keep the same business model, and focus on iterative innovations in the core to show progress with small wins. At the same time, they pursue a second kind of innovation at the edge. Ultimately the exponential organization at the core can outgrow the main organization.”

Six Ds of exponential organizations

Leadbetter explained how Diamandis divided technology into six phases of emergence and adoption.

“Empowering any product or service with technology gets it on an exponential curve,” he said. “In phase one, it’s digitized. Second is the deceptive phase when it gets smaller, cheaper and more powerful over time but it hasn’t emerged yet. Phase three is disruptive when it’s too late to get into the market if you’re not already there. The fourth phase is dematerialization, the way things have disappeared off our desks and onto our computers and phones. The fifth phase is demonetized where everything is less expensive, and phase six is democratized, when companies can have the same impact as governments, and individuals can have the same impact as companies.”

“Millennials would rather do their banking with Facebook and Amazon than traditional banks.”

Move from scarcity to abundance-based model

Social robotics were among the most fascinating emerging technologies Leadbetter discussed. He said a bank in Australia was investing with partners in robots that are learning how to interact with people, and will provide a different experience for consumers in retail banks. A bank in India is working on something similar, where a robot welcomes customers, asks them what they want, and guides them to right place. Eventually the robot will manage transactions.

“Millennials would rather do their banking with Facebook and Amazon than traditional banks, and that’s a problem for established institutions,” said Leadbetter. “Banks are challenged because the financial services industry is in the deceptive phase.”

The fundamental challenge for banks is to flip from a scarcity-based business model to one of abundance. “Banks have operated like we have a scarce commodity, and we’ll give you access to it and that’s how we make money,” said Leadbetter. “That’s no longer the case. Every company has to think like a software company, years ahead of where they are now, managing the business simultaneously on three horizons — defend and extend the current core business, build momentum for emerging business, and create viable options for the future.”

Instead of hiring outside consultants for innovation, Leadbetter recommended companies build a network of specialist coaches, and engage in “a 10-week sprint process to define the problem, and iterate solutions and results.” Besides understanding the technology itself, the established financial community needs to embrace innovation as a business imperative. Disruptive upstarts and demanding consumers won’t have it any other way.

Artificial Intelligence: What’s Now And Next In IoT-Driven Supply Chain Innovation

As with most people, coffee is one of the most important rituals in my morning routine. There’s something about the aroma and taste that kick-starts my ability to have a great day. So imagine my surprise when a favorite coffee shop was closed before I had to jump on an early-morning flight. The employees were in the shop, but the gate locked out coffee aficionados, like me, who really needed that jolt of caffeine.

Although this experience was understandably a letdown, it was also a source of inspiration. Think about it: How many times has your business been “locked out” of an opportunity to change? You see the advantages that can help your operation move forward and accomplish great things; however, there’s something that’s keeping you from crossing that threshold and succeeding.

Such is the case for supply chain automation initiatives. Although the Internet of Things (IoT) is playing a significant role in today’s supply chain, advanced analytics-driven data aggregation platforms are now earmarked as an area to watch. IDC recently reported that 60% of manufacturers will likely leverage an advanced analytics-driven data aggregation platform to improve the speed and accuracy of the fulfillment process this year. However, Gartner gives a stern warning that three out of five factory-level artificial intelligence (AI) initiatives in large global companies will likely stall within the next three years due to inadequate skill sets.

Four AI opportunities that can strengthen your IoT-driven supply chain

IDC’s and Gartner’s predictions are stunning revelations considering the skyrocketing growth of computing capacity and data volumes used to empower supply chain leaders to make smarter decisions. But if done well, supply chain managers can extend their IoT capabilities with artificial intelligence to run operations that are fast, nimble, and intelligent enough to stay competitive in today’s high-speed global marketplace.

1. Extend your IoT platform to build a smarter supply chain

As IoT devices get increasingly smaller and more prevalent in every asset along the supply chain, an impressive volume of data is not fully leveraged – leaving much of the insight it contains in the dark. Personally, I think this common problem is not a problem at all. Instead, it’s a sign that the IoT is maturing to a point where AI is the natural next step to discover and use real-time information in the best way possible.

For example, when a new customer signs a contract, production planning can start automatically. A digital signature triggers warehouses to pick and ship goods needed as outlined in the agreement. Then production is scheduled, and qualified and available human resources are assigned by a system. If employees need to travel to a customer location to install a machine, arrangements are made in parallel. Through AI, the best rates for hotels, flights, and car rentals and dates that fit into everyone’s schedule and time restrictions can be determined immediately.

Once installed, the machine can use Big Data algorithms to learn patterns and behaviors. This approach enables them to detect the threat of malfunctions that require maintenance, define factors impacting performance, and optimize processes and opportunities for automation.

The struggle against complexity is something that plagues the mind of supply chain managers. From a growing network of suppliers and the risk of corrupt sourcing practices to trade restrictions and just-in-time delivery, automation can help them sleep better at night. Center for Global Enterprise research reveals that the more digital the supply chain, the greater the chance the business can reduce procurement costs by 20%, lower supply chain process costs by 50%, and increase revenue by 10%.

For example, beverage powerhouse Schweppes Australia combated the inaccuracies and inefficiencies of its supply chain by upgrading its entire distribution center management system with AI technology. The paperless system gave supply chain managers greater visibility into every task – from sourcing to last-mile delivery – to eliminate inefficient practices such as over-replenishing pick faces, which often led to delivery delays and suboptimal spend.

By introducing more flexible and efficient practices, Schweppes’ supply chain processes are now 99.9% accurate. Managers monitor shipments, at the click of a button, to pinpoint and evaluate gaps in replenishment, prioritize and sequence order drops, and oversee process status. As a result, the company streamlined its order shipment process to picking by bulk, transferring orders to the staging line, dropping actual orders, and retrieving orders from the staging line.

3. Maximize the potential of every employee involved in supply chain processes

The use of robots in the supply chain is a hot topic. But contrary to widespread fears, the real news is not about eliminating human jobs – it’s about making work more meaningful and challenging for everyone while offsetting a looming labor shortage. In fact, IDC predicted that 50% of fulfillment centers will have co-bots operating next to humans in the picking, packing, and shipping floor to drive productivity up 30% and lower the cost of operations.

For retail heavyweight Amazon, deploying an army of over 30,000 Kiva robots across a few of its warehouses in 2014 saved roughly US$22 million. And if Deutsche Bank is correct, the company will pick up an additional $800 million in savings as more plants are given the opportunity to use the technology.

4. Turn your supply chain into a source for value-add services

Supply chain management should prepare for the future by implementing the IoT and defining new use cases to tap into never-conceived revenue streams. And if there was a reason to get started, hygiene company Hagleitner is an excellent source of inspiration.

For years, Hagleitner has been a reliable bathroom supplier for fast-food restaurants, hospitals, and theaters throughout Austria as well as multiple cruise lines internationally. As the demand for its services grew, the company decided to make its operations more efficient by embedding sensors to track everything from the use of faucets to stock levels of soap, air freshener, and paper towels.

This strategy not only made services more responsive, proactive, and consistent, but the company is also saving warehouse space, meeting demand with greater sustainability, and optimizing logistics processes and personnel assignments. At the same time, its customers are assured that their bathrooms are well-equipped and address every visitor’s bathroom needs.

Artificial intelligence: A natural step in supply-chain innovation

The more supply chain technology matures, the smarter the supply chain will run. While the IoT is helping your supply chain respond faster and more flexibly to market changes, it is still important to look ahead and see how the data you’re generating can take your supply chains to new levels of efficiency, demand forecasting, and speed.

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About Marcell Vollmer

Marcell Vollmer is the Chief Digital Officer for SAP Ariba (SAP). He is responsible for helping customers digitalize their supply chain. Prior to this role, Marcell was the Chief Operating Officer for SAP Ariba, enabling the company to setup a startup within the larger SAP business. He was also the Chief Procurement Officer at SAP SE, where he transformed the global procurement organization towards a strategic, end-to-end driven organization, which runs SAP Ariba and SAP Fieldglass solutions, as well as Concur technologies in the cloud. Marcell has more than 20 years of experience in working in international companies, starting with DHL where he delivered multiple supply chain optimization projects.

The Differences Between Machine Learning And Predictive Analytics

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.

Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.

Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.

Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

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About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data.
He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics.
Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past.

The Blockchain Solution

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.

It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.

It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.

By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

In the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

Blockchain will provide the foundation of automated trust for all parties in the supply chain.

The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.

3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.

Big Data management tools will process all the information streaming in around the clock from IoT sensors.

AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $1 per item saves you more than $52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Blockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.

In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.

Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.

The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.

Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!

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Why Blockchain Is Crucial For FP&A: Part 1

In these times of almost continuous technological change, there is a natural tendency to be suspect of whatever is being heralded as the “flavor of the month” or the “next best bet.” In early 2017, I was graciously given the opportunity to speak on what I believed to be the technologies that were transforming finance and specifically, the FP&A function. The talk I ended up giving covered five areas:

Advanced analytics and forecasting

Robotic process automation

Cloud and Software-as-a-Service

Artificial intelligence

Blockchain

While all these topics deserve further investigation, for this article, I want to focus on blockchain. Part of the reason for diving deeper into blockchain is the lack of understanding of what it actually is and the great amount of time people in the finance function are currently spending talking about it. This has greatly changed in the past nine months.

Last March, while hosting an FP&A Roundtable in Boston, I ask a group of 25 senior FP&A professionals how familiar they were with the concept of blockchain. Out of this august group, there was only one participant who felt truly comfortable with the concept. I still get asked on a regular basis, all over the world, “Blockchain. What is it?”

Blockchain: What is it?

By allowing digital information to be distributed but not copied, blockchain technology has created the spine of a new type of Internet. Picture a spreadsheet that is duplicated thousands of times across a network of computers. Now imagine that this network is designed to regularly update this spreadsheet, and you have a basic understanding of blockchain.

Information held on a blockchain exists as a shared and continually reconciled database. This is a way of using the network that has obvious benefits. The blockchain database isn’t stored in any single location, meaning the records it keeps are truly transparent and easily verifiable. No centralized version of this information exists for someone to corrupt. Hosted by many computers simultaneously, its data is accessible to any authorized user.

Blockchain technology is like the Internet in that it has a built-in robustness. By storing blocks of information that are identical across its network, the blockchain 1) cannot be controlled by any single entity and 2) has no single point of failure. The Internet itself has proven to be durable for almost 30 years. It’s a track record that bodes well for blockchain technology as it continues to be developed.

A self-auditing ecosystem

The blockchain network lives in a state of consensus, one that automatically checks in with itself on a regular basis. A kind of self-auditing ecosystem of a digital value, the network reconciles every transaction that happens at regular intervals. Each group of these transactions is referred to as a “block.” Two important properties result from this:

Transparency. Data is embedded within the network as a whole, and by definition, is available to all authorized users.

Incorruptibility. Altering any unit of information on the blockchain would mean using a huge amount of computing power to override the entire network. In theory, it is possible; however, in practice, it’s unlikely to happen.

A decentralized technology

By design, the blockchain is a decentralized technology, so anything that happens on it is a function of the network as a whole. Some important implications stem from this. By creating a new way to verify transactions, aspects of traditional commerce may become unnecessary.

Today’s Internet has security problems that are familiar to everyone. However, by storing data across its network, the blockchain eliminates the risks that come with data held centrally. There are no centralized points of vulnerability that can be exploited. In addition, while we all currently rely on the “username/password” system to protect our identity and assets online, blockchain security methods use encryption technology.

I hope this little tutorial helps describe what blockchain is. In my next article, I’ll discuss the value of blockchain to the FP&A profession.

2018 will be a busy year with FP&A Roundtables in St. Louis, Charlotte, Atlanta, San Diego, Las Vegas, London, Boston, Minneapolis, DFW, San Francisco, Hong Kong, Jeddah, and many other locations around the world to support the global FP&A community.

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About Brian Kalish

Brian Kalish is founder and principal at Kalish Consulting. As a public speaker and writer addressing many of the most topical issues facing treasury and FP&A professionals today, he is passionately committed to building and connecting the global FP&A community. He hosts FP&A Roundtable meetings in North America, Europe, Asia, and South America.
Brian is former executive director of the global FP&A Practice at AFP. He has over 20 years experience in finance, FP&A, treasury, and investor relations. Before joining AFP, he held a number of treasury and finance positions with the FHLB, Washington Mutual/JP Morgan, NRUCFC, Fifth Third Bank, and Fannie Mae.
Brian attended Georgia Tech in Atlanta, GA for his undergraduate studies and the Pamplin College of Business at Virginia Tech for his graduate work. In 2014, Brian was awarded the Global Certified Corporate FP&A Professional designation.